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基于约束因子图割的主动轮廓法在RNA干扰筛选中的细胞图像自动分割

Constraint factor graph cut-based active contour method for automated cellular image segmentation in RNAi screening.

作者信息

Chen C, Li H, Zhou X, Wong S T C

机构信息

Department of EEIS, University of Science and Technology of China, Hefei, PR China.

出版信息

J Microsc. 2008 May;230(Pt 2):177-91. doi: 10.1111/j.1365-2818.2008.01974.x.

Abstract

Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of such experiments generates a large number of images with great variations in image quality, which makes manual analysis unreasonably time-consuming. Therefore, effective techniques for automatic image analysis are urgently needed, in which segmentation is one of the most important steps. This paper proposes a fully automatic method for cells segmentation in genome-wide RNAi screening images. The method consists of two steps: nuclei and cytoplasm segmentation. Nuclei are extracted and labelled to initialize cytoplasm segmentation. Since the quality of RNAi image is rather poor, a novel scale-adaptive steerable filter is designed to enhance the image in order to extract long and thin protrusions on the spiky cells. Then, constraint factor GCBAC method and morphological algorithms are combined to be an integrated method to segment tight clustered cells. Compared with the results obtained by using seeded watershed and the ground truth, that is, manual labelling results by experts in RNAi screening data, our method achieves higher accuracy. Compared with active contour methods, our method consumes much less time. The positive results indicate that the proposed method can be applied in automatic image analysis of multi-channel image screening data.

摘要

基于图像的高通量全基因组RNA干扰(RNAi)实验越来越多地被开展,以促进对复杂生物过程中基因功能的理解。此类实验的自动筛选会生成大量图像质量差异很大的图像,这使得人工分析耗时过长。因此,迫切需要有效的自动图像分析技术,其中分割是最重要的步骤之一。本文提出了一种用于全基因组RNAi筛选图像中细胞分割的全自动方法。该方法包括两个步骤:细胞核和细胞质分割。先提取并标记细胞核以初始化细胞质分割。由于RNAi图像质量相当差,设计了一种新颖的尺度自适应可控滤波器来增强图像,以便在多刺细胞上提取细长的突起。然后,将约束因子GCBAC方法和形态学算法结合成一种综合方法来分割紧密聚集的细胞。与使用种子分水岭法得到的结果以及真实情况(即RNAi筛选数据中专家的手动标注结果)相比,我们的方法具有更高的准确性。与主动轮廓法相比,我们的方法耗时少得多。积极的结果表明,所提出的方法可应用于多通道图像筛选数据的自动图像分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e818/2839415/a38cb8a450da/nihms182041f1.jpg

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